MFFDet: Enhancing Multi-Scale Forest Fire Detection in UAV Imagery

In Unmanned aerial vehicle (UAV) forest fire detection, flames and smoke exhibit dramatic scale variations. Existing methods often struggle with multi-scale feature extraction, fusion quality, and localization reliability, resulting in limited accuracy improvements. To address this issue, this study optimizes the backbone, neck, and head of YOLOv11n to propose a novel multi-scale forest fire detector (MFFDet), which consists of three key modules: (1) the Multi-Scale Feature Calibration Module (MFCM) is designed to improve multi-scale feature representation by context aggregation and detail calibration; (2) the Cross-Scale Semantic Alignment Module (CSAM) is proposed to enhance fusion quality by applying channel reorganization and local spatial refinement; and (3) the Location Quality Estimator Head (LQEH) is presented for reliable localization by mapping the statistical information of regression distributions into a localization quality score, which systematically boosts the accuracy and stability of multi-scale object detection. In addition, to alleviate the scarcity of UAV forest fire detection data, this study constructs a UAV Forest Fire Dataset (UF${}^2$D), providing important data support for UAV-based fire detection. Experiments on UF${}^2$D show that MFFDet achieves an mAP@0.5 of 70.1%, the best among all compared models, representing a 4.4% improvement over the baseline. Moreover, it attains the top performance on small, medium, and large objects, with AP${}_s$ of 20.3%, AP${}_m$ of 31.5%, and AP${}_l$ of 44.8%, highlighting MFFDet’s robustness and accuracy for multi-scale flame and smoke detection in a complex forest fire environment, which bears important practical significance for the intelligent upgrade of forest fire prevention and control.